Khlood M. Mehdar1, Toufique A. Soomro2,3,*, Ahmed Ali4, Faisal Bin Ubaid5, Muhammad Irfan6,*, Sabah Elshafie Mohammed Elshafie1, Aisha M. Mashraqi7, Abdullah A. Asiri8, Nagla Hussien Mohamed Khalid8, Hanan T. Halawani7
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3091-3132, 2025, DOI:10.32604/cmes.2025.065471
- 30 June 2025
Abstract Breast cancer remains one of the most pressing global health concerns, and early detection plays a crucial role in improving survival rates. Integrating digital mammography with computational techniques and advanced image processing has significantly enhanced the ability to identify abnormalities. However, existing methodologies face persistent challenges, including low image contrast, noise interference, and inaccuracies in segmenting regions of interest. To address these limitations, this study introduces a novel computational framework for analyzing mammographic images, evaluated using the Mammographic Image Analysis Society (MIAS) dataset comprising 322 samples. The proposed methodology follows a structured three-stage approach. Initially,… More >